Artificial Intelligence as an Enabler for the Real Kind: Interview with Kit Cox, CEO at Enate

We’ve heard much about the applications for generative AI in the creative fields, such as writing, design, and software development. But what does the technology have to offer for operations and services, and what safeguards are needed? Kit Cox of Enate sets out his views. 

Can you tell us more about your background and how you became interested in the automation industry?  

I’ve been into technology from a young age. I started coding at the age of 10 when this became available on BBC Bitesize and my interest in automation evolved from there. I’m an engineer by trade and love to bring my manufacturing mindset into the services world – literally building solutions to complex problems. At Enate, we’ve built a SaaS platform that really enables businesses to get their operational ducks in a row, giving them full visibility over their end-to-end processes so they can see where the gaps are, streamline processes, and deliver work on time. We’ve harnessed the latest automation technology and now AI, too, to help those businesses ultimately work smarter and faster and become more agile.  

How would you describe the generative AI revolution and its potential impact on society? 

I was around when the internet was launched, when mobile was launched, even when personal computing started and I can say that AI is the biggest single change in technology capability that I’ve seen in my lifetime. The scale of it is rapid and game-changing.  

The impact on society will be profound and, as it stands right now, it risks being negative. We are all being warned about existential risks around it and, it’s true, there are. There are risks that exist right this second with the ability to generate misinformation quickly and influence people, politics, and democracy. Major elections are coming up in the US and the UK next year that risk being severely disrupted by generative misinformation. It’s not just verbal generative that’s the risk, it’s images and videos that have never existed – deep fakes – that pose a big risk.  

Many people are concerned about job losses due to AI. Can you elaborate on why you believe the scaremongering around this issue may not be as severe as it seems? 

In terms of the economy, it’s true that certain jobs won’t exist any more and some roles will be disrupted. It would be irresponsible to be founding a technology company and say there won’t be an impact on jobs. Of course there will be an impact and we need to support people through that. We shouldn’t be supporting people who have a laissez-faire attitude to job losses and shrug when asked; this is people’s lives.  

There is talk of universal basic income, but my concern with this is that it runs the risk of taking us back to the Middle Ages in terms of control and living in a society of institutionalised serfdom.  

Technology has always removed and created jobs, but the upside is that right now there are more jobs than people can physically do. There’s only so much that machines can take away; you still need people. What’s perfectly clear, though, is that as a society we need more productivity in the market to make the economy run better, and AI can help fulfil that ask. There will be many jobs that are AI-enabled rather than replaced.   

As the founder of Enate, could you explain how your platform combines real intelligence (people) and artificial intelligence to deliver and manage services? 

Most of the current excitement around AI is geared towards tools that enable the creators of the world (writers, designers, software developers) to speed up productivity but, when it comes to service operations, these are often too variable or complicated to automate. Operations sit in end-to-end processes and, until now, using AI in service operations has required installing costly and time-intensive machine-learning pipelines which often deliver only 70 per cent accuracy.  

What we’ve done differently at Enate is to build our very own AI product which sits on top of our existing orchestration platform and automatically categorises emails, extracts data, performs sentiment analysis, understands foreign languages, and asks and answers specific questions such as, “Is this email just a thank you?” There’s no need for expensive or complex MLOps, as EnateAI is automatically integrated into the platform. Plus, it’s 90 per cent accurate and free.  

In high-volume email environments, such as shared mailboxes, approximately 5-10 per cent of the effort is spent on manual triage. Employees spend time figuring out the content of the email and identifying the person best suited to work on it. With EnateAI capabilities, customers can reduce 20 per cent of their manual efforts and provide better service to their clients as a result.  

What all this means is that service employees and businesses can spend less time and effort on boring, repetitive tasks and more time on customer success. We see this as the end of service and the start of success, which is not to service the customer, but to delight the customer. If we do a great job of moving work between people and digital workers, and allowing that blend to change, then we’re creating capacity in the people for them to do extraordinary things rather than just ordinary things. 

The recent announcements from Google, Adobe, and IBM indicate increasing support for the generative AI revolution. In your opinion, what measures and tools are necessary to enable widespread adoption of intelligent automation? 

The way I think about it is that if your job includes something like creating, e.g., graphic designer or copywriter, then using generative AI right now is low-risk and those roles should be adopting it. If you’re a writer, you should use GPT; if you’re a designer, use Midjourney.  

However, if your job title includes words like “delivery”, “process” or “execute”, then it’s important to put guard rails around it and manage risk, which is where something like orchestration comes in. You need a way of measuring the outcome you’re expecting, and you need a crystal-clear policy around data, how to manage it, and what the appetite is to use your organisation’s data in training other models. I would go as far as to say that you probably need to appoint someone in your business to be responsible for AI safety. A chief AI officer, for instance, is going to be required very soon. It’s a very specific skill set for organisations who want to get serious about AI.   

How do you foresee the economic impact of AI in the coming years? Are there any potential risks or challenges that need to be addressed to ensure positive outcomes? 

AI is essentially a natural resource that everyone who uses the internet has helped to create and has a stake in. It needs to be decentralised so that it benefits society as a whole, and not just the handful of geeks who figured out how to soak all that knowledge up and create AI products out of it.  

One of the big risks with AI is that capitalism breaks if the relationship that has existed for the last 100 years between labour and those with capital gets broken. There is a real risk around economics.

What are some key factors that businesses should consider before implementing AI and automation within their workplaces? How can they ensure a successful integration and maximise the benefits? 

To train and use AI models, you need to share data with them. Start by getting a grip on your data management rules. Clearly define what data can and can’t be shared with public and private AI models. Make sure your entire team is aware of and following these rules.  

Brainstorm and identify use cases for AI. Collaborate with your staff and an external expert to explore the art of what’s possible to do using AI. Remember that what is possible is literally changing weekly right now. This is the fastest pace of technology-driven change in my lifetime. Classify your use cases into four categories: skill support, transaction support, customer support, and decision support. 

All these use cases support humans in their tasks.  

Investigate AI safety, hallucination, and data privacy before deploying AI, especially generative AI like GPT-4.  

Adopt the following playbook for each category. 

Skill support  

1. Identify everyone in the organisation whose job involves creating (writing, designing, and building). 
2. Create task forces for each skill and let them find the best AI co-pilot for their task. 
3. Test and procure low-risk tools to support individuals in your organisation.

Transaction support  

1. Orchestrate your workflows. It’s the only way to manage and control the deployment and mitigate the risk of “doing the wrong thing faster”.  
2. Use your orchestrator to create business cases for AI deployment for specific tasks.  
3. Choose the right AI tool for each job, such as NLP, IDP, or NLG.  
4. Give your AI tools access to core business systems data to improve outcomes.  
5. Ensure that people are always in control, particularly when using NLG technologies such as GPT-4.  

Customer support  

1. Ensure that your organisation has the skills to use conversational AI safely.  
2. Choose a customer dialogue platform that meets your use cases.  
3. Use sophisticated human-in-loop technology to flip conversations between AI and people.  
4. Give the conversational AI access to data that will help it converse with customers more effectively, but start with non-sensitive product knowledge. 

With the rapid advancement of AI technologies, what kind of legislation do you believe is necessary to ensure the longevity and ethical use of generative AI?  

There is legislation that needs to be put in place to recognise that this is a natural resource that only exists because every human has created the data that’s allowed these AI models to be trained. The economic benefit of these generative models therefore sits with every human who has had access to the internet at any point over the last 20 years, rather than just with a handful of smart geeks.  

In the near future, what notable progress or breakthroughs do you expect to see in the realm of intelligent automation and its fusion with AI? Are there any specific sectors or domains where you predict that the effects will be exceptionally revolutionary?  

I believe there are some old technologies that are going to go from expensive and valuable to totally commoditised very rapidly. People currently spend a lot of money on intelligent document processing, which will soon become a commodity. When we start integrating general models and use them for practical tasks rather than just pure information, there are going to be huge breakthroughs. Once these powerful AI models can access other systems and perform actions going from reasoners to generate, the game will change.  

There could be extraordinary impacts in this way on accounting and legal professions, but it’s likely that regulators will prevent it, as it’s not in their interest to change it.  

Medicine is an area ripe for disruption. I predict that the diagnostic parts of being a doctor will largely be automated and the caring and communication aspects which can’t be emulated by AI will be more important requirements of the role. This will open up medicine to people who have the empathetic and caring skills needed for medicine but may have previously been locked out due to not being able to pass traditional exams. I see general practitioners becoming much more powerful as fundamental carers. We’re already seeing it with the change in the NHS staffing plan that’s being drawn up indicating that some doctors won’t need to go to medical school.   

About Enate 

Enate is an end-to-end orchestration platform designed to help businesses run operations smoothly and produce consistent work on time, View, manage, and track the flow of all work, identify automation opportunities, assign tasks to the right resource, and become more efficient.  

EnateAI powered by GPT-4 is the latest product release from Enate. It’s integrated into the platform and offers five exciting features to help businesses leverage AI in operations including:  

Categorise – automatically categorise emails into or create the right ticket category. 

Data extraction – extract data from your emails and auto-populate forms. 
Sentiment analysis – identify the emotional tone of communications from your clients. 

“Thank you” analysis – know whether incoming “thank you” emails need action. 

Foreign-language fluency – understand and process foreign-language emails.  

Executive Profile

Kit Cox founded Enate to help businesses run their operations smoothly. Enate provides an end-to-end orchestration solution to help businesses manage, organize, distribute and deliver work on time. Global businesses such as EY and TMF have adopted Enate’s SaaS solution to manage end-to-end services.